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Chest X-ray becomes one of the most common medical diagnoses due to itsnoninvasiveness. The number of chest X-ray images has skyrocketed, but readingchest X-rays still have been manually performed by radiologists, which createshuge burnouts and delays. Traditionally, radiomics, as a subfield of radiologythat can extract a large number of quantitative features from medical images,demonstrates its potential to facilitate medical imaging diagnosis before thedeep learning era. With the rise of deep learning, the explainability of deepneural networks on chest X-ray diagnosis remains opaque. In this study, weproposed a novel framework that leverages radiomics features and contrastivelearning to detect pneumonia in chest X-ray. Experiments on the RSNA PneumoniaDetection Challenge dataset show that our model achieves superior results toseveral state-of-the-art models (> 10% in F1-score) and increases the model'sinterpretability.

Pneumonia Detection on Chest X-ray using Radiomic Features and Contrastive Learning